The number of known human polymorphisms is growing rapidly and the cost, both in time and money, for ascertaining genotypes has decreased substantially. The result can potentially drive forward the search for genes affecting human phenotypes with positive consequences for understanding, avoiding and treating disease. It will also impact the understanding of drug response and the avoidance of allergic reactions to drugs. While the rate of data gathering has advanced so significantly, the statistical methods to deal with such large and complex data sets have not kept pace. The most pressing problem is that of how to test for association between phenotypes and a vast array of potentially associated loci. Most current methods require multiple testing and suffer the associated loss of statistical power. This problem is further complicated as the loci, and hence the tests, are not independent which leads to over correction. Pairwise measures of linkage disequilibrium, haplotype blocks and hot spots inadequately describe the patterns of associations between the allelic states at proximal loci. More sophisticated coalescent and population genetic models have problems of tractability or of fully incorporating the information from haplotype samples. Graphical modeling provides a statistical framework for characterizing precisely this sort of complex stochastic data. This empirical approach can provide concise, accurate and tractable representations of the joint distribution of alleles at proximal loci. This is directly relevant to such problems as detecting association with phenotypic variables and selecting informative subsets of loci. The great potential of this approach is that categorical phenotypes can be included in the same analysis and association with polymorphisms assessed jointly with the inter locus associations. This proposal is to extend graphical modeling methods already developed by the investigators for haploid data to diploid data, larger genomic regions, admixed populations and family data.